Workflow Optimization for Parallel Split Learning

arXiv:2402.10092v1 Announce Type: cross
Abstract: Split learning (SL) has been recently proposed as a way to enable resource-constrained devices to train multi-parameter neural networks (NNs) and participate in federated learning (FL). In a nutshell, SL splits the NN model into parts, and allows clients (devices) to offload the largest part as a processing task to a computationally powerful helper. In parallel SL, multiple helpers can process model parts of one or more clients, thus, considerably reducing the maximum training time …

abstract arxiv cs.dc cs.lg cs.ni devices federated learning networks neural networks nns optimization part processing train type workflow workflow optimization